Dynamic join processing using real time merged notification listener

ABSTRACT

Described are methods, systems and computer readable media for dynamic join operations.

This application claims the benefit of U.S. Provisional Application No.62/161,813, entitled “Computer Data System” and filed on May 14, 2015,which is incorporated herein by reference in its entirety.

Embodiments relate generally to computer data systems, and moreparticularly, to methods, systems and computer readable media for thedynamic updating of join operations.

Joining two tables to create a third table has historically requiredcombining large sets of data that can tax even large local memory storesand fast processors. Also, standard joins may not provide a user withthe desired results. Also, standard joins may require combining largesets of data again after a small change to one of the joined tables toupdate the result.

Embodiments were conceived in light of the above mentioned needs,problems and/or limitations, among other things.

Some implementations can include a memory and processor efficientcomputer system for dynamic updating of join operations, the systemcomprising one or more processors, computer readable storage coupled tothe one or more processors, the computer readable storage having storedthereon instructions that, when executed by the one or more processors,cause the one or more processors to perform operations. The operationscan include sending a digital request for a remote query processor froma client computer to a remote query processor on a query servercomputer. The operations can also include at the remote query processor,performing operations including automatically connecting the clientcomputer to the remote query processor via the digital communicationsnetwork. The operations can include receiving a join-based querydigitally from the client computer to the remote query processor thatcontains two or more input tables to be joined. The operations can alsoinclude adding a node for each table providing input to the joinoperation to the update propagation graph. The operations can furtherinclude adding a join operation results node to the update propagationgraph for holding results of executing the join-based query. Theoperations can also include adding a real-time merged notificationlistener for the join operation node in the update propagation graph.The operations can include applying the join operation to the two ormore input tables using indexes from the two or more input tables toidentify and retrieve data needed for the join operation in order tominimize local memory and processor usage. The operations can alsoinclude using the real-time merged notification listener for the joinoperation node to listen for any changes to the joined two or more inputtables in order to minimize local memory and processor usage by onlyconducting a join operation when a change has been detected. Theoperations can further include when the real-time merged notificationlistener receives notification of changes to any of the joined two ormore input tables, using indexes from the two or more input tables toapply the join operation only to the changes to update the joinoperation results node only for changed index ranges in order tominimize local memory and processor usage.

The operations can include wherein the join-based query is a left_joinresulting in a table that has one column for each of a plurality ofcolumns in a first input table's columns, and one or more newcorresponding second input table columns with names that do not overlapor are renamed in order to not overlap with a name of one or morecolumns from a first input table. The operations can also include theone or more new columns containing an aggregation of all values from thesecond input table that match a join criteria. The operations canfurther include the types of all newly created second input tablecolumns not involved in the join criteria being an array of the secondinput table's column type.

The operations can include wherein the join-based query is an as_of_joinresulting in a table that has one column for each of a plurality ofcolumns in a first input table's columns, and one or more newcorresponding second input table columns with names that do not overlapor are renamed in order to not overlap with a name of one or morecolumns from a first input table. The operations can also include theone or more new columns containing all values from the second inputtable that match a join criteria, the join criteria performing an exactmatch on N−1 match columns followed by performing a closest-less-thanmatch on the last match column.

The operations can include wherein the join-based query is areverse_as_of_join resulting in a table that has one column for each ofa plurality of columns in a first input table's columns, and one or morenew corresponding second input table columns with names that do notoverlap or are renamed in order to not overlap with a name of one ormore columns from a first input table. The operations can also includethe one or more new columns containing all values from the input tablethat match a join criteria, the join criteria performing an exact matchon N−1 match columns followed by performing a closest-greater-than matchon the last match column.

The operations can include wherein the join-based query is arange_as_of_join resulting in a table that has one column for each of aplurality of columns in a first input table's columns, and one or morenew corresponding second input table columns with names that do notoverlap or are renamed in order to not overlap with a name of one ormore columns from a first input table. The operations can also includethe one or more new columns containing all values from the input tablethat match a join criteria, the join criteria returning each cell in theone or more new columns with an array of all values within a designatedrange for N-M match columns where the match is exact, and M matchcolumns define a range match.

The operations can include wherein the join-based query is anatural_join resulting in a table that has one column for each of aplurality of columns in a first input table's columns, and one or morenew corresponding second input table columns with names that do notoverlap or are renamed in order to not overlap with a name of one ormore columns from a first input table. The operations can also includethe table having a same number of rows as the source table, the samenumber of rows containing an original content of the source table rows.The operations can further include the one or more new columnsdetermined by matching one or more values from the input table with thesource table.

The operations can include wherein the join-based query is an exact_joinresulting in a table that has one column for each of a plurality ofcolumns in a first input table's columns, and one or more newcorresponding second input table columns with names that do not overlapor are renamed in order to not overlap with a name of one or morecolumns from a first input table. The operations can also include thetable having a same number of rows as the source table, the same numberof rows containing an original content of the source table rows. Theoperations can further include the one or more new columns determined bymatching one or more values from the input table with the source table.The operations can also include the table containing exactly one matchfor each row with the input table.

The operations can include wherein the join-based query creates a subsetfiltered by a match criteria on a full Cartesian product, resulting in atable that has one column for each of a plurality of columns in a firstinput table's columns, and one or more new corresponding second inputtable columns with names that do not overlap or are renamed in order tonot overlap with a name of one or more columns from a first input table.

The operations can include wherein the join operation node is differentthan the join operation results node.

The operations can include wherein the real-time merged notificationlistener for the join operation node is separate from the join operationnode.

The operations can include wherein the real-time merged notificationlistener for the join operation node is separate from the join operationresults node.

The operations can include wherein the operations of the remote queryprocessor further include returning join operation results with strictordering to guarantee ordering.

The operations can include wherein the operations of the remote queryprocessor further include returning the join operation results that cancontain arrays mapped to data.

The operations can include wherein the strict ordering is according totime.

The operations can include wherein the strict ordering is dictated by anorder of data in the two or more input tables.

The operations can include wherein the changes include one or more of anadd, modify, delete, or re-index.

The operations can include wherein the operations of the remote queryprocessor further comprise automatically re-applying the join operationwhen the real-time merged notification listener detects any one of anadd, modify, delete, or re-index message.

The operations can include further comprising when the two or more inputtables are derived from a same ancestor table, changes in the sameancestor table cause a cascade of change notifications through theupdate propagation graph causing the remote query processor to combinethe change notifications for efficiency and consistency.

The operations can include wherein the automatically re-applying is onlyapplied to changed portions of the two or more input tables and not tounchanged portions.

The operations can include wherein the join criteria includes a formula.

Some implementations can include a method for dynamic updating of joinoperations, the method comprising sending a digital request for a remotequery processor from a client computer to a remote query processor on aquery server computer. The method can also include automaticallyconnecting the client computer to the remote query processor via thedigital communications network. The method can further include receivinga join-based query digitally from the client computer to the remotequery processor that contains two or more input tables to be joined. Themethod can also include adding a node for each table providing input tothe join operation to the update propagation graph. The method caninclude adding a join operation results node to the update propagationgraph for holding results of executing the join-based query. The methodcan also include adding a real-time merged notification listener for thejoin operation node in the update propagation graph. The method caninclude applying the join operation to the two or more input tablesusing indexes from the two or more input tables to identify and retrievedata needed for the join operation in order to minimize local memory andprocessor usage. The method can also include using the real-time mergednotification listener for the join operation node to listen for anychanges to the joined two or more input tables in order to minimizelocal memory and processor usage by only conducting a join operationwhen a change has been detected. The method can further include when thereal-time merged notification listener receives notification of changesto any of the joined two or more input tables, using indexes from thetwo or more input tables to apply the join operation only to the changesto update the join operation results node only for changed index rangesin order to minimize local memory and processor usage.

Some implementations can include a nontransitory computer readablemedium having stored thereon software instructions that, when executedby one or more processors, cause the one or more processors to performoperations. The operations can include sending a digital request for aremote query processor from a client computer to a remote queryprocessor on a query server computer. The operations can also include atthe remote query processor, performing operations. The operations caninclude automatically connecting the client computer to the remote queryprocessor via the digital communications network. The operations canalso include receiving a join-based query digitally from the clientcomputer to the remote query processor that contains two or more inputtables to be joined. The operations can further include adding a nodefor each table providing input to the join operation to the updatepropagation graph. The operations can also include adding a joinoperation results node to the update propagation graph for holdingresults of executing the join-based query. The operations can includeadding a real-time merged notification listener for the join operationnode in the update propagation graph. The operations can also includeapplying the join operation to the two or more input tables usingindexes from the two or more input tables to identify and retrieve dataneeded for the join operation in order to minimize local memory andprocessor usage. The operations can further include using the real-timemerged notification listener for the join operation node to listen forany changes to the joined two or more input tables in order to minimizelocal memory and processor usage by only conducting a join operationwhen a change has been detected. The operations can also include whenthe real-time merged notification listener receives notification ofchanges to any of the joined two or more input tables, using indexesfrom the two or more input tables to apply the join operation only tothe changes to update the join operation results node only for changedindex ranges in order to minimize local memory and processor usage.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example computer data system showing anexample data distribution configuration in accordance with someimplementations.

FIG. 2 is a diagram of an example computer data system showing anexample administration/process control arrangement in accordance withsome implementations.

FIG. 3 is a diagram of an example computing device configured for remotequery processor processing in accordance with some implementations.

FIG. 3A is a diagram of an example query server host data sources inaccordance with some implementations.

FIG. 3B is a diagram of an example query server host in accordance withsome implementations.

FIG. 4 is a diagram of an example tree-based table storage in accordancewith some implementations.

FIG. 4A is a diagram of example basic query components in accordancewith some implementations.

FIG. 4B is a diagram of an example update propagation graph for joinoperations in accordance with some implementations.

FIG. 5 is a flowchart of an example join operation update in accordancewith some implementations.

FIG. 5A is a diagram of an example dynamic update of a join inaccordance with some implementations.

FIG. 6 is a flowchart of an example remote query processor joinoperation in accordance with some implementations.

FIG. 7 is a diagram of an example as_of_join in accordance with someimplementations.

FIG. 8 is a diagram of an example left_join in accordance with someimplementations.

FIG. 9 is a diagram of an example reverse_as_of_join in accordance withsome implementations.

FIG. 10 is a diagram of an example range_as_of_join in accordance withsome implementations.

FIG. 11 is a diagram of an example natural_join in accordance with someimplementations.

FIG. 12 is a diagram of an example exact_join in accordance with someimplementations.

FIG. 13 is a diagram of an example join in accordance with someimplementations.

DETAILED DESCRIPTION

Reference is made herein to the Java programming language, Java classes,Java bytecode and the Java Virtual Machine (JVM) for purposes ofillustrating example implementations. It will be appreciated thatimplementations can include other programming languages (e.g., groovy,Scala, R, Go, etc.), other programming language structures as analternative to or in addition to Java classes (e.g., other languageclasses, objects, data structures, program units, code portions, scriptportions, etc.), other types of bytecode, object code and/or executablecode, and/or other virtual machines or hardware implemented machinesconfigured to execute a data system query.

FIG. 1 is a diagram of an example computer data system and network 100showing an example data distribution configuration in accordance withsome implementations. In particular, the system 100 includes anapplication host 102, a periodic data import host 104, a query serverhost 106, a long-term file server 108, and a user data import host 110.While tables are used as an example data object in the descriptionbelow, it will be appreciated that the data system described herein canalso process other data objects such as mathematical objects (e.g., asingular value decomposition of values in a given range of one or morerows and columns of a table), TableMap objects, etc. A TableMap objectprovides the ability to lookup a Table by some key. This key representsa unique value (or unique tuple of values) from the columns aggregatedon in a byExternal( ) statement execution, for example. A TableMapobject is can be the result of a byExternal( ) statement executed aspart of a query. It will also be appreciated that the configurationsshown in FIGS. 1 and 2 are for illustration purposes and in a givenimplementation each data pool (or data store) may be directly attachedor may be managed by a file server.

The application host 102 can include one or more application processes112, one or more log files 114 (e.g., sequential, row-oriented logfiles), one or more data log tailers 116 and a multicast key-valuepublisher 118. The periodic data import host 104 can include a localtable data server, direct or remote connection to a periodic table datastore 122 (e.g., a column-oriented table data store) and a data importserver 120. The query server host 106 can include a multicast key-valuesubscriber 126, a performance table logger 128, local table data store130 and one or more remote query processors (132, 134) each accessingone or more respective tables (136, 138). The long-term file server 108can include a long-term data store 140. The user data import host 110can include a remote user table server 142 and a user table data store144. Row-oriented log files and column-oriented table data stores arediscussed herein for illustration purposes and are not intended to belimiting. It will be appreciated that log files and/or data stores maybe configured in other ways. In general, any data stores discussedherein could be configured in a manner suitable for a contemplatedimplementation.

In operation, the input data application process 112 can be configuredto receive input data from a source (e.g., a securities trading datasource), apply schema-specified, generated code to format the loggeddata as it's being prepared for output to the log file 114 and store thereceived data in the sequential, row-oriented log file 114 via anoptional data logging process. In some implementations, the data loggingprocess can include a daemon, or background process task, that isconfigured to log raw input data received from the application process112 to the sequential, row-oriented log files on disk and/or a sharedmemory queue (e.g., for sending data to the multicast publisher 118).Logging raw input data to log files can additionally serve to provide abackup copy of data that can be used in the event that downstreamprocessing of the input data is halted or interrupted or otherwisebecomes unreliable.

A data log tailer 116 can be configured to access the sequential,row-oriented log file(s) 114 to retrieve input data logged by the datalogging process. In some implementations, the data log tailer 116 can beconfigured to perform strict byte reading and transmission (e.g., to thedata import server 120). The data import server 120 can be configured tostore the input data into one or more corresponding data stores such asthe periodic table data store 122 in a column-oriented configuration.The periodic table data store 122 can be used to store data that isbeing received within a time period (e.g., a minute, an hour, a day,etc.) and which may be later processed and stored in a data store of thelong-term file server 108. For example, the periodic table data store122 can include a plurality of data servers configured to store periodicsecurities trading data according to one or more characteristics of thedata (e.g., a data value such as security symbol, the data source suchas a given trading exchange, etc.).

The data import server 120 can be configured to receive and store datainto the periodic table data store 122 in such a way as to provide aconsistent data presentation to other parts of the system.Providing/ensuring consistent data in this context can include, forexample, recording logged data to a disk or memory, ensuring rowspresented externally are available for consistent reading (e.g., to helpensure that if the system has part of a record, the system has all ofthe record without any errors), and preserving the order of records froma given data source. If data is presented to clients, such as a remotequery processor (132, 134), then the data may be persisted in somefashion (e.g., written to disk).

The local table data server 124 can be configured to retrieve datastored in the periodic table data store 122 and provide the retrieveddata to one or more remote query processors (132, 134) via an optionalproxy.

The remote user table server (RUTS) 142 can include a centralizedconsistent data writer, as well as a data server that providesprocessors with consistent access to the data that it is responsible formanaging. For example, users can provide input to the system by writingtable data that is then consumed by query processors.

The remote query processors (132, 134) can use data from the data importserver 120, local table data server 124 and/or from the long-term fileserver 108 to perform queries. The remote query processors (132, 134)can also receive data from the multicast key-value subscriber 126, whichreceives data from the multicast key-value publisher 118 in theapplication host 102. The performance table logger 128 can logperformance information about each remote query processor and itsrespective queries into a local table data store 130. Further, theremote query processors can also read data from the RUTS, from localtable data written by the performance logger, or from user table dataread over NFS.

It will be appreciated that the configuration shown in FIG. 1 is atypical example configuration that may be somewhat idealized forillustration purposes. An actual configuration may include one or moreof each server and/or host type. The hosts/servers shown in FIG. 1(e.g., 102-110, 120, 124 and 142) may each be separate or two or moreservers may be combined into one or more combined server systems. Datastores can include local/remote, shared/isolated and/or redundant. Anytable data may flow through optional proxies indicated by an asterisk oncertain connections to the remote query processors. Also, it will beappreciated that the term “periodic” is being used for illustrationpurposes and can include, but is not limited to, data that has beenreceived within a given time period (e.g., millisecond, second, minute,hour, day, week, month, year, etc.) and which has not yet been stored toa long-term data store (e.g., 140).

FIG. 2 is a diagram of an example computer data system 200 showing anexample administration/process control arrangement in accordance withsome implementations. The system 200 includes a production client host202, a controller host 204, a GUI host or workstation 206, and queryserver hosts 208 and 210. It will be appreciated that there may be oneor more of each of 202-210 in a given implementation.

The production client host 202 can include a batch query application 212(e.g., a query that is executed from a command line interface or thelike) and a real time query data consumer process 214 (e.g., anapplication that connects to and listens to tables created from theexecution of a separate query). The batch query application 212 and thereal time query data consumer 214 can connect to a remote querydispatcher 222 and one or more remote query processors (224, 226) withinthe query server host 1 208.

The controller host 204 can include a persistent query controller 216configured to connect to a remote query dispatcher 232 and one or moreremote query processors 228-230. In some implementations, the persistentquery controller 216 can serve as the “primary client” for persistentqueries and can request remote query processors from dispatchers, andsend instructions to start persistent queries. For example, a user cansubmit a query to 216, and 216 starts and runs the query every day. Inanother example, a securities trading strategy could be a persistentquery. The persistent query controller can start the trading strategyquery every morning before the market opened, for instance. It will beappreciated that 216 can work on times other than days. In someimplementations, the controller may require its own clients to requestthat queries be started, stopped, etc. This can be done manually, or byscheduled (e.g., cron) jobs. Some implementations can include “advancedscheduling” (e.g., auto-start/stop/restart, time-based repeat, etc.)within the controller.

The GUI/host workstation can include a user console 218 and a user queryapplication 220. The user console 218 can be configured to connect tothe persistent query controller 216. The user query application 220 canbe configured to connect to one or more remote query dispatchers (e.g.,232) and one or more remote query processors (228, 230).

FIG. 3 is a diagram of an example computing device 300 in accordancewith at least one implementation. The computing device 300 includes oneor more processors 302, operating system 304, computer readable medium306 and network interface 308. The memory 306 can include a remote queryprocessor application 310 and a data section 312 (e.g., for storingASTs, precompiled code, etc.).

In operation, the processor 302 may execute the remote query processorapplication 310 stored in the memory 306. The remote query processorapplication 310 can include software instructions that, when executed bythe processor, cause the processor to perform operations for executingand updating queries in accordance with the present disclosure (e.g.,performing one or more of 502-526, 550-572, 602-612 described below).

The remote query processor application program 310 can operate inconjunction with the data section 312 and the operating system 304.

A varied set of join operations can provide a powerful toolset to usersfor manipulating data with one join command versus the use of severaljoins or looping code. Each join in a set of joins can be built forparticular types of input tables to provide a desired type of result.

FIG. 3A is a diagram of an example query server host 320 with associateddata stores in accordance with at least one embodiment. A query serverhost 320 can contain one or more remote query processors 322 (asdescribed at 310) and high speed memory, for example shared RAM 336 plusaccess to medium access speed memory 346 and slow access speed storage354.

The remote query processor 322 can contain one or more processors 324and high speed memory 326 such as RAM. The high speed memory 326 cancontain one or more update propagation graphs 328, one or more tableindexes 330, in memory data 332, and recent data cache 334. The highspeed memory 326 can request and retrieve data from one or more slowaccess speed storages 355 and/or from high speed memory 336.

The high speed memory 336 can be memory that is shared with one or moreremote query processors 322 and one or more table data cache proxies(not shown). The high speed memory 336 can contain one or more datacolumns, for example, a symbol column data 338, a date column data 340,a time column data 342, and a quote column data 344. The high speedmemory 336 can exchange data with remote query processor 322 high speedmemory 326 and/or medium access speed memory 346, and can request andreceive data from slow access speed storage 355.

The medium access speed memory 346 can contain one or more data columns,for example, symbol column data 348, a date column data 350, a timecolumn data 352, and a quote column data 354. Medium access speed memory346 can exchange data with high speed memory 336 and transmit data to aslow access speed storage 355.

The slow access speed storage 355, for example, a file server with oneor more hard drives, can contain one or more source columns, forexample, a symbol column source 358, a date column source 360, a timecolumn source 362, and a quote column source 364. The one or more columnsource can be copied into medium speed solid state storage 356, forexample, flash, to provide faster access for more frequently accesseddata.

FIG. 3B is a diagram of an example query server host 370 as described at320 in accordance with at least one embodiment. A query server host cancontain one or more remote query processors (372, 374, 376) associatedwith one or more table data cache proxy clients (378, 380, 382), ashared memory 384 as described at 336 that can exchange data (386, 388,390) with the table data cache proxy clients (378, 380, 382), and one ormore table data cache proxies 392 that can exchange data with the sharedmemory 384.

In general, some implementations can include a computer data system thatstores and retrieves data (e.g., time series data) according to strictordering rules. These rules ensure that data is stored in a strict orderand that results of a query are evaluated and returned in the same ordereach time the query is executed. This can provide an advantage ofoptimizing the query code for query execution speed by permitting a userand query process (e.g., a remote query processor) to rely on anexpected ordering and eliminate a need for performing an additionalsorting operation on query results to achieve an expected or neededordering for downstream operations. It also allows data to be orderedaccording to the source's data publication order without necessarilyincluding data elements to refer to for query evaluation or resultordering purposes. It should be noted that updates from real-time orchanging data, however, may not always be seen in the same order, sincedata is processed after asynchronous notifications and according torefresh cycles that progress at different speed and frequency indistinct remote query processors or client processes. Updates are notnecessarily the results of a query, though. For some implementationsorder within a partition is always maintained.

For example, in the real-time (or periodic) case, a data system maystore data in arrival order (which is typically time-series order)within the partition of the table that corresponds to a given datasource. In the permanent-store case (or long term storage case), thecomputer data system starts with the real-time order and thenre-partitions, optionally groups, and optionally sorts the real-time (orperiodic) data according to one or more columns or formulas, otherwiserespecting the retrieval order for the real-time data when producing thenew stored data and its ordering.

Some implementations can include a partitioned data store that haspartitions based, at least in part, on a file system and can includephysical machine partitions, virtual machine partitions and/or filesystem directory structure partitions. For example, partitions A, B andC of a data store (e.g., a column data source) may reside in differentdirectories of a file system. In addition to different directories, thedata store may be distributed across a plurality of data servers(physical or virtual) such that the data is partitioned to a givenserver and within that server, the data may be sub-partitioned to one ormore directories, and within each directory, the data may be furtherpartitioned into one or more sub-directories and/or one or more files.

Partitioning the data using a file system provides an advantage in thatthe location keys and retrieval instructions for storage locations ofinterest for potential query result data can be discovered by means oftraversing a directory structure, rather than a separately-maintainedlocation key and location retrieval information discovery service. Oncediscovered, locations can be narrowed from the full set of locations toa sub-set according to query instructions, which can help speed up queryoperations by permitting the data system to defer accessing actual data(“lazy loading”) and begin to narrow down the set of rows to evaluatewithout handling data (e.g., in memory and/or transmitting via acommunication network). This is further enhanced by support in the datasystem's query engine for partitioning columns—columns of the data thatare a property of all rows in any location retrieved from a givenpartition of the location key space, typically embodied in the name of asub-directory when a file system is used in this way. Certain queryoperations can thus be executed in whole or in part against location keyfields on a per-partition basis rather than against column data on aper-row basis. This may greatly improve execution performance bydecreasing the input size of the calculations by several orders ofmagnitude.

Within a partition, data may be grouped according to a column value. Thegrouping may have one or more levels, with a multi-level grouping havinga logical hierarchy based on the values of two or more columns, suchthat groups in “higher-level” columns fully-enclose groups in“lower-level” columns. Further, within a partition or group, the datacan be ordered according to a given ordering scheme, e.g. strictly bythe real-time recording order, or according to some sorting criteria.Grouping in this way can enhance query performance by allowing for verysimple, high performance data indexing, and by increasing the physicallocality of related data, which in turn can reduce the number of rows orblocks that must be evaluated, and/or allow for extremely performantdata caching and pre-fetching, with high cache hit ratios achieved withsmaller cache sizes than some other data systems.

For example, securities trading data may be partitioned across serversby a formula that takes ticker symbol as input. Within each server, thedata may be partitioned by a directory corresponding to trade data date.Within each date partition directory, data may be in a file grouped byone or more ticker symbol values. Within each ticker symbol group, thedata may be ordered by time.

In another example, when generating a query result table, the datasystem can first focus on a server (or servers) for the symbol (orsymbols) being accessed, then one or more partitions for the date(s) ofinterest, then one or more files and group(s) within the file(s) beforeany data is actually accessed or moved. Once the data system resolvesthe actual data responsive to the query, the data (or references to thedata in one or more data sources) can be retrieved and stored into aquery result table according to a strict ordering and will be evaluatedand returned in that same order each time the query is executed.

It will be appreciated that some data stores or tables can include datathat may be partitioned, grouped, and/or ordered. For example, some datamay be partitioned and ordered, but not grouped (e.g., periodic datasuch as intraday trading data). Other data may be partitioned, groupedand ordered (e.g., long-term storage data such as historical tradingdata). Also it will be appreciated that any individual table, partitionor group can be ordered. Partitions can be grouped according to agrouping and/or ordering specific to each partition.

FIG. 4 is a diagram of an example tree-based table storage 400 inaccordance with at least one embodiment. Tables, especially largetables, can benefit from a hierarchical tree-based structure as shown in400. The tree root 402 can be a table handle. Underneath the table root402 can be a series of partition columns (404, 406, 408). Thepartitioning can be implemented in a filesystem, object store or thelike. The partition columns (404, 406, 408) can be visible to a user orhidden from a user. For example, a column could be partitioned by dateand each partition could contain data for a single date, such as2016-03-18. In this example, the date can be a table column visible to auser. The partition columns can also be used to divide the workload formaintaining a column over more than one fileserver.

The leaf nodes of a partition column can be subtables. An examplesubtable structure is shown at 410. In a subtable structure 410, data inthe form of a subtable 418 can be stored for all rows and columns of atable.

For example, a table can have a logical table schema of columns forDate, Ticker Symbol, Timestamp, Bid Price and Ask Price. In thisexample, two partition columns can be created under the table root, onepartition for Date and one partition for FileServer. The Date partitioncolumn (for example, 404) can contain directory paths to data for asingle date, such as 2016-03-18. Because the data is all of the samedate, 2016-03-18, the subtable 418 does not need to contain a Datevalue. In this example, the data 418 for the same date, 2016-03-18, canbe spread across multiple file servers. A second partition column (forexample, 406) is set under the Date partition column in the tree toprovide a path, such as <table>/<date>/<fileserver>, to locate all theDate data for 2016-03-18. As noted earlier in this example, the Datepartition column can be visible to a user, but a fileserver partitioncolumn may not be visible.

The data partition column is visible to the user to help the userformulate queries that can take advantage of the tree structure. Forexample, query performance can be enhanced by applying filters, such aswhere clauses, in an order based on the location of the data in a tree.Generally, applying the filter to a partition column closer to the tableroot 402 can minimize the amount of data processed to arrive at a finalresult. For example, in the Date, Ticker Symbol, Timestamp, Bid Price,Ask Price example, the most efficient filtering order is Date followedby Ticker Symbol. In this example, table.where (“Date=d”, “Sym=‘AAPL’”,“Bid>1000”) can be much faster than table.where (“BID>1000”,“Sym=‘AAPL’”, “Date=d”). In table.where (“Date=d”, “Sym=‘AAPL’”,“Bid>1000”), only the subtables 418 under the date “d” partition columnneeds to be retrieved for processing because the subtables 418 in thisexample are already partitioned by date, the system does not need toprovide any additional filtering work for date. In contrast table.where(“BID>1000”, “Sym=‘AAPL’”, “Date=d”) can require every bid for everystock ticker for every date to be retrieved and processed because the“BID>1000” is processed first, and a partition column for “BID>1000” maynot exist. As shown by this example, partition columns can be used toprovide a built-in filter option that does not require the system tore-filter per each query the filters on the contents of the partitioncolumns.

It will be appreciated that if the user had placed “Sym=‘AAPL’” before“BID>1000” in the where statement, the system could have filtered on agrouping by ticker symbols to more efficiently locate AAPL before thenfinding bids greater than 1000. Without using the group by tickersymbols first, all bids greater than 1000 would be retrieved.

It will also be appreciated that partition columns are not limited toDate or Fileserver. Any common attribute that would provide performancegains if pre-filtered can be a good candidate for partition columns.

It will also be appreciated that query performance gains can be achievedby creating grouping columns (412, 414, 416) underneath the Datepartition columns. For example, a grouping column could be created foreach distinct ticker symbol.

It will be further appreciated that the system can process each filterand determine which column each filter depends on. Then, based uponwhere the columns are located in the tree structure, the system can rankthe filters based upon how much of the tree the system removes forfuture filters. For example, when processing date, symbol, and bidcolumns, date can be the highest in the tree (partition column) followedby Symbol (grouping column) followed by Bid (normal column). If 3filters are submitted by a user that has dependencies on the date,symbol, and bid columns, the system can make an educated guess at theorder the clauses can best be executed for maximum efficiency. Forexample, given t1.where(“Bid>10”,“Symbol=‘AAPL’”,“Date=today( )”), thesystem can reorder to t1.where(“Date=today( )”,“Symbol=‘AAPL’”,“Bid>10”)to maximize efficiency.

FIG. 4A is a diagram an example of basic query components in accordancewith at least one embodiment. A remote query processor 420 can contain aone or more processors 422 and memory 424. A remote query processor 420memory 424 can contain one or more update propagation graphs 426. Anupdate propagation graph 426 can contain a graphical node representationof a query such as a join operation on two tables (t1 and t2) to createa third table (t3).

It will be appreciated that an update propagation graph can containdynamic nodes that are table objects that can be updated frequently overtime as well as static nodes that do not change over time.

A remote query processor 420 can exchange data with one or morehistorical data 430 sources and/or one or more real-time data 432sources. A remote query processor 420 can also receive query tasks fromone or more user query applications 428 and provide results back to oneor more user query applications 428.

It will be appreciated that a remote query processor 420 can provide aclient computer with an address assignment of the remote queryprocessor, the address assignment identifying a specific port of theremote query processor 420 on a query server computer available to theclient computer to connect over a digital communications network. Theremote query processor 420 can automatically connect the client computerto the remote query processor via the digital communications network.

FIG. 4B is a diagram of an example update propagation graph for joinoperations 440 in accordance with some implementations. A node for table1 442 and a node for table 2 444 can represent table objects that can bejoined by a join operation 446 to create a table 3 containing the joinresults 448. An update propagation graph for join operations 440 cancontain a table 1 listener 443, a table 2 listener 445, and real-timemerged notification listener 450. A table 1 listener 443 can listen forchanges to table 1 442 and a table 2 listener 445 can listen for changesto table 2 444. A real-time merged notification listener 450 can listenfor one or more changes propagated from table 1 442 and table 2 444 thatcan then be joined by the join operation 446 through a table 1 listener443 and a table 2 listener 445, respectively. When the real-time mergednotification listener 450 is notified by, for example, an add, delete,modify, re-index, or other message, the table 3 join results 448 can beupdated for those changes by executing the join operation 446 on thechanges that occurred to table 1 442 and/or table 2 444.

It will be appreciated that a real-time merged listener can be asoftware construct that listens for change notifications, such as add,delete, modify, or re-index messages, or other message types propagateddown the update propagation graph. In a real-time environment, changescan happen frequently, for example, every millisecond, second, minute,hour, etc.

It will be appreciated that table 1 442 and table 2 444 can be derivedfrom a common ancestor table. For example, if 442 and 444 share a commonancestor, changes in the ancestor can trigger a cascade of add, modify,delete, or re-index (AMDR) messages through an update propagation graph,which can ultimately cause both 442 and 444 to create AMDR messages. Thesystem can recognize that an ancestor caused both 442 and 444 to sendAMDR messages to a join. Before creating its own AMDR message, thesystem join (various nodes and merge listener) can combine the AMDRmessages for efficiency and consistency. The ultimate AMDR from thesystem join can then give a time-consistent view of processing allinformation simultaneously.

FIG. 5 is a flowchart of an example join operation update in accordancewith some implementations. Processing can begin at 502 and/or 504, whena remote query processor receives a notification of changes to table 1through add, modify, delete, or re-index (AMDR) messages, or othermessage types, and/or a remote query processor receives a notificationof changes to table 2 through AMDR messages, or other message types.

It will be appreciated that because table 3 has already been created bya join operation on tables 1 and 2 before 502 and 504 that any change totable 1 or table 2 will require an update to the join to update table 3.Processing continues to 506.

At 506, based on the changes to table 1 and/or table 2, the remote queryprocessor determines row changes for table 3. Processing continues to508.

At 508, for the row changes to be applied to table 3, table 1 and table2 data that is needed to compute the row for table 3 is loaded.Processing continues to 510.

At 510, a determination is made by the remote query processor as towhether the needed data is in memory. If the data is in memory,processing continues to 522. If the data is not in memory processingcontinues to 512.

At 512, a determination is made by the remote query processor as towhether the needed data is in high speed cache. If the data is in highspeed cache, processing continues to 522. If the data is not in highspeed cache, processing continues to 514.

At 514, a determination is made by the remote query processor as towhether the needed data is available from a table data cache proxy(TDCP). If the data is available from a TDCP, processing continues to516. If the data is not available from a TDCP, processing continues to518.

At 516, a determination is made by the remote query processor as towhether the needed data is in the TDCP cache. If the data is in the TDCPcache, processing continues to 522. If the data is not in the TDCPcache, processing continues to 520.

At 520, data is requested form an intraday server. Processing continuesto 522.

At 518, data is loaded from a file server and/or file server cache.Processing continues to 522.

At 522, data is retrieved from the location where the data was found.Processing continues to 523.

At 523, if the cache is full, enough data is evicted from the cache tomake space for the retrieved data. Processing continues to 524.

At 524, the updated row for table 3 is computed according to the joincriteria. Processing returns back to 508 to continue the update cycleand continues to 526.

At 526, nodes below table 3 in the update propagation graph (child nodesof table 3) are notified of the changes to table 3.

FIG. 5A is a flowchart of an example dynamic update of a join operationto table 1 and table 2 to update table 3 in accordance with someimplementations. Processing can begin at 552 and/or 554, when an updatepropagation graph receives notification of changes to either table 1and/or table 2 through AMDR messages to table 1 and/or table 2 objectsin the update propagation graph within the update propagation graphclock cycle.

It will be appreciated that table 1 and table 2 can be derived for acommon ancestor data store, such as a table as discussed in the FIG. 4Bsection above. Processing continues to 556.

At 556, the remote query processor receives notification of changes totable 1 and/or table 2. Processing continues to 558.

At 558, the remote query processor uses the table 1 and table 2 objectsfrom the update propagation graph and the table 1 and table 2 AMDRupdate messages to determine the data that needs to be used in a joinoperation for updating table 3. Processing continues to 560.

At 560, the remote query operation determines the location of dataneeded for table 1 and table 2 for updating table 3. Processingcontinues to 562 and 564.

At 562, the location of table 1 data is determined to be located ineither persistent (e.g. on-disk) column sources, remote query processormemory, such as RAM, or a table data cache proxy (TDCP). Processingcontinues to 570 if the location is column sources, to 568 if thelocation is TDCP, or to 566 if the location is remote query processormemory, such as RAM.

It will be appreciated that not all column sources or rows may berequired to perform an update. The system defer loading of data until aparticular section of data required to either perform the join operationor is requested by a downstream consumer of the table.

At 564, the location of table 2 data is determined to be located ineither column sources, remote query processor memory, such as RAM, or atable data cache proxy (TDCP). Processing continues to 570 if thelocation is column sources, to 568 if the location is TDCP, or to 566 ifthe location is remote query processor memory, such as RAM.

At 566, data is retrieved from the remote query processor memory, suchas RAM. Processing continues to 572.

At 568, data is retrieved from TDCP cache or intraday server. Processingcontinues to 572.

At 570, data is retrieved from table column sources flash or columnsources storage.

It will be appreciated that any arbitrary storage hierarchy can be used.Processing continues to 572.

At 572, the remote query processor performs a join operation on table 1and table 2 column sources by re-computing the necessary rows and thensending the results to the update propagation graph.

It will be appreciated the t3 can be added to the update query graphwhen the query is first executed. After the initial execution of thequery, messages can be passed to a child after an update.

FIG. 6 is a flowchart of an example remote query processor join actionin accordance with some implementations. Processing begins at 602 when aremote query processor receives a request from a client machine toperform a join operation on two or more input tables.

It will be appreciated that a join operation can include withoutlimitation, an as_of_join, left_join, a reverse_as_of_join, arange_as_of_join, a natural_join, an exact_join, or a join. Processingcontinues to 604.

At 604, the remote query processor adds a node for each table providinginput to the join operation to the update propagation graph. Processingcontinues to 606.

At 606, the remote query processor adds a node to the update propagationgraph for the join operation resulting table. Processing continues to608.

At 608, the remote query processor adds a real-time merged notificationlistener to the join operation node to listen for changes to the joinedtables. Processing continues to 610.

At 610, the real-time merged notification listener listens for changesto any of the tables used in the join operation. Processing continues to612.

At 612, when the real-time merged notification listener receivesnotification of changes to any of the tables used in the join operation,the join operation is applied to capture the changes and apply thechanges to the join operation resulting table.

It will be appreciated that a match for a join operation can be based ona formula.

FIG. 7 is a diagram of an example as_of_join in accordance with someimplementations. In this example, a join operation can be used onTable_A (leftTable) and Table_B (rightTable) to create Table_C. The joinoperation in this example is an as_of_join 720. An exemplary commandstring for an as_of_join can be Table_C=leftTable aj(Table rightTable,String columnsToMatch, String columnsToAdd). The command can cause thesystem to look up columns in the rightTable that meet the matchconditions in the columnsToMatch list. The columnsToMatch can be a commaseparated list of match conditions such as “leftColumn=rightColumn” or“columnFoundInBoth”, with the last match condition meaning really“leftColumn matches the highest value of rightColumn that is<=leftColumn” or “leftTable.columnFoundInBoth matches the highest valueof rightTable.columnFoundInBoth that is <=leftTable.columnFoundInBoth”.Matching is done exactly for the first n−1 columns and with less-than(e.g., via a binary search with a saved cursor to improve adjacentlookups) for the last match pair. The columns of the leftTable can bereturned intact, together with the columns from the rightTable definedin a comma separated list “columnsToAdd”. The separated list“columnsToAdd” can be a comma separated list with columns form therightTable that need to be added to the leftTable as a result of amatch, expressed either as columnName or newColumnName=oldColumnName ifrenaming is desired or necessary. The keys of the last column to matchshould be monotonically increasing in the rightTable for any existingcombination of the previous n−1 match columns. If more than one rowmatches, then any one of the matching rows may be selected. Which row isselected can be decided by the search algorithm.

In the as_of_join example shown in FIG. 7, leftTable table_A 702 isas_of_joined with rightTable Table_B 712 with an as_of_join command 720that creates the resultant table, Table_C 732. In this example, thevalues for ticker 704, price 706, and TradeTime 708 columns from Table_Aremain the same in Table_C as ticker 734, price 736, TradeTime 738. TheTradeTime 716 column from rightTable Table_B 712 is renamed in Table_C732 as TradeTimeB 740. The MidPrice 718 column in Table_B 712 retainsthe same column name, MidPrice 742. In this example, the A1, $100, 9:30first row in Table_A 702 does not have a match in Table_B 712 becauseevery time value in TradeTime 716 for A1 is greater than 9:30.Accordingly, in Table_C 732, the TradeTimeB 740 and MidPrice 742 columnscontain NULL values for the A1, $100, 9:30 row.

FIG. 8 is a diagram of an example left_join in accordance with someimplementations. In this example, a join operation can be used onTable_A and Table_B to create Table_C. The join operation in thisexample is a left_join. An exemplary command string for a left_join canbe Table_C=leftTable leftJoin(Table rightTable, String columnsToMatch,String columnsToAdd).

The left_join operation can return a table that has one column for eachof the leftTable's columns, and one column corresponding to each of therightTable columns whose name does not overlap or are renamed in orderto not overlap with the name of a column from the leftTable.

The new columns (those corresponding to the rightTable) can contain anaggregation of all values from the leftTable that match the joincriteria. Consequently, the types of all rightTable columns not involvedin a join criteria, is an array of the rightTable column type. If thetwo tables have columns with matching names, then the method can failwith an exception unless the columns with corresponding names are foundin one of the matching criteria. A left_join operation does notnecessarily involve an actual data copy, or an in-memory table creation.

It will be appreciated that the values for columns in a result tablederived from a right table need not immediately be computed, but can begenerated on demand when a user requests the values.

In the left_join example shown in FIG. 8, leftTable table_A 802 is leftjoined with rightTable table_B 812 with a left_join command 820 thatcreates the resultant table, table_C 832. In this example, the valuesfor column 1 804, column 2 806, and column 3 808 columns from table_Aremain the same in table_C 832. The A1, B1, C1; A1, B2, C2; and A2, B5,C8 rows of table_A have matches in column 1 814 of table_B. The table_Arow of A3, B9, C11 does not find an A3 match in table_B and the resultis an empty array in column 4 840 of table_C 832. Alternativeembodiments may instead use a sentinel result value instead of an emptyarray (e.g., NULL). Because two rows exist for A1 in table_B 812, a twovalue array of “E1” and “E3” is created in column 4 840 of table_C 832.A2 has one value in table_B 312 and thus has a single value array incolumn 4 840 of table_C.

FIG. 9 is a diagram of an example reverse_as_of_join in accordance withsome implementations. In this example, a join operation can be used onTable_A and Table_B to create Table_C. The join operation in thisexample is a reverse_as_of_join. An exemplary command string for areverse_as_of_join can be Table_C=leftTable raj(Table rightTable, StringcolumnsToMatch, String columnsToAdd). The reverse_as_of_join canfunction as the reverse of the as_of_join operation. In comparison tothe as_of_join operation selecting the previous value, thereverse_as_of_join operation can select the next value. For example, thereverse_as_of_join operation can select the value that is greater thanor equal to rather than less than or equal to the timestamp.

In the reverse_as_of_join example shown in FIG. 9, leftTable table_A 902is reverse_as_of_joined with rightTable tableB 912 with areverse_as_of_join command 920 that creates the resultant table, table_C932. In this example, the values for ticker 904, price 906, andtradetime 908 columns from table_A remain the same in table_C 932 asticker 934, price 936, tradetime 938. The tradetime 916 column fromrightTable table_B 912 is renamed in Table_C 932 as tradetimeB 940. Themidprice 918 column in table_B 912 retains the same column name,midprice 942. In this example, the A1, $101, 9:40 and A1, $99, 16:00rows in table_A 902 do not have a match in table_B 912 because everytime value in tradetime 916 for A1 is less than 9:40. Accordingly, intable_C 932, the tradetimeB 940 and midprice 942 columns contain NULLvalues for the A1, $101, 9:40 and A1, $99, 16:00 rows.

FIG. 10 is a diagram of an example range_as_of_join in accordance withsome implementations. In this example, a join operation can be used onTable_A and Table_B to create Table_C. The join operation in thisexample is a range_as_of_join 1020. The range_as_of_join can be acombination of an as-of-join, a reverse-as-of-join, and a left_join. Therange-as-of-join can search for a range of rows in a rightTable. Therecan be several alternatives for specifying the range to be matched inthe rightTable. One possible syntax for specifying the range to bematched can be to match columns C1 . . . CN. C1 . . . CN-2 can be exactmatches. CN-1 can be a range matching column that indicates the start ofthe range in the right table, CN can be a range matching column thatindicates the end of the range in the rightTable. An exemplary commandstring for a range_as_of_join can bet3=t1.rangeJoin(t2,“A,B,StartTime=Time,EndTime=Time”, “Time,C”), whichcan create result types such as:

A—AType from t1

B—BType from t1

StartTime—DBDateTime from t1

EndTime—DBDateTime from t1

Time—Array{DBDateTime} from t2

C—Array{CType} from t2

One possible syntax for specifying the range to be matched can be tomatch columns columns C1 . . . CN with C1 . . . CN-1 being exactmatches. CN can be a range matching column. A separate argument canindicate how the range will be computed. The range can be a combinationof a time-period (e.g. five minutes before/after), a row count (e.g., 10rows before), or a formula (e.g., include all prior/subsequent rows aslong as a formula is true).

An exemplary command string for a range_as_of_join can bet3=t1.rangeJoin(t2, “A,B,Time”, Period(′05:00′), Count(1),“Time2=Time,C”), which can create result types such as:

A—AType from t1

B—BType from t1

Time—DBDateTime from t1

Time2—Array{DBDateTime} from t2

C—Array{CType} from t2

Another exemplary command string for range_as_of_join can bet3=t1.rangeJoin(t2, “A,B,Time”, Count(‘0’), Formula(‘C>D’),“Time2=Time,C,D”), which can create result type such as:

A—AType from t1

B—BType from t1

Time—DBDateTime from t1

Time2—Array{DBDateTime} from t2

C—Array{CType} from t2

D—Array {DType} from t2

In this example, the range can include all rows subsequent to Time int1; until C is not greater than D.

It will be appreciated that an index from a leftTable can be reused, andall leftTable columns can be passed through to the result table, andthat rightTable arrays do not need to be stored.

In the range_as_of_join example shown in FIG. 10, leftTable table_A 1002is range_as_of_joined with rightTable table_B 1012 with arange_as_of_join command 1020 that creates the resultant table, table_C1032. In this example, the values for ticker 1004, price 1006, andtradetime 1008 columns from table_A remain the same in table_C 1032 asticker 1034, price 1036, tradetime 1038. The tradetime 1016 column formrightTable table_B 1012 is renamed in Table_C 1032 as tradetimeB 1040.The midprice 1018 column in table_B 1012 retains the same column name,midprice 1042. In this example, the A1, $99, 16:00 row in table_A 1002does not have a match in table_B 1012 because every time value intradetime 1016 for A1 is not within the period 5, 10 (5 minutes beforeto 10 minutes after) range. Accordingly, in table_C 1032, the tradetimeB1040 and midprice 1042 columns can contain either a NULL value or anempty array for the A1, $99, 16:00 row.

FIG. 11 is a diagram of an example natural_join in accordance with someimplementations. In this example, a join operation can be used onTable_A and Table_B to create Table_C. The join operation in thisexample is a natural_join 1120. An exemplary command string for anas_of_join can be Table_C=leftTable naturalJoin(Table rightTable, StringcolumnsToMatch, String columnsToAdd). Table_C can have the exact samenumber of rows as the leftTable with all the columns from the leftTablewith the exact original content. The rightTable can be expected to haveone or no rows matching the columnsToMatch constraints. ColumnsToMatchcan be comma separated constraints, expressed either as columnName (ifthe names are identical) or columnNameFromA=columnNameFromB. Theresulting table, Table_C can contain one column for each columnspecified by columnToAdd, containing the matching rightTable values ornull. ColumnsToAdd can be comma separated columns from B to be added tothe final result, expressed either as columnName ornewColumnName=oldColumnName when renaming the column is desired ornecessary.

In the natural_join example shown in FIG. 11, leftTable employee table1102 is natural joined with rightTable department table 1112 with anatural_join command 1120 that creates the resultant table, table_C1132. In this example, the values for last name 1104 and department ID806 from employee table remain the same in table_C 1132. Each of thedepartment ID 1106 values in employee table 1102 have correspondingdepartment ID 1114 values in department table 1112 with the exception ofthe last row of employee table 1102, “John” and “36”. Because a valuefor “36” does not exist in the department ID 1114 column of thedepartment table 1112, the row for “John” and “36” in table_C has a NULLvalue for department name 1138.

FIG. 12 is a diagram of an example exact_join in accordance with someimplementations. In this example, a join operation can be used on asecurities table 1202 and a view of the securities table to createTable_C 1232. The join operation in this example is an exact_join 1220.An exemplary command string for an as_of_join can be Table_C=leftTableexactJoin(Table table, String columnsToMatche, String columnsToAdd).

An exact_join can function identical to a natural_join with theexception that an exact_join expects exactly one match for each of itscolumns with the rightTable.

It will be appreciated that one method to ensure a match for each columnis to join a table with a view of itself.

In the exact_join example shown in FIG. 12, leftTable securities table1202 is exact joined with a view of securities table 1202 with anexact_join command 1220 that creates the resultant table, table_C 1232.In this example, the underlying ticker symbol 1242 is added to the rowcontaining the ticker 1236 symbol for a derivative product of theunderlying ticker symbol 1241.

FIG. 13 is a diagram of an example join in accordance with someimplementations. In this example, a join operation can be used onTable_A and Table_B to create Table_C. The join operation in thisexample is a join 1320. An exemplary command string for a join can betable_C=leftTable.join (rightTable, String columnsToMatch, StringcolumnsToAdd), which can return the join of the leftTable with therightTable. The result can be defined as the outcome of first taking theCartesian product (or cross-join) of all records in the tables(combining every record in the leftTable with every record in therightTable, with optional renamings of rightTable columns induced bycolumnToAdd)—then returning all records which satisfy the matchconstraints, with all the columns of leftTable and the columns ofrightTable in columnsToAdd as selected columns. ColumnsToMatch can becomma separated constraints, expressed either as columnName (when thecolumn names in leftTable and rightTable are the same) orcolumnNameFromleftTable=columnNameFromrightTable. ColumnsToAdd can becomma separated columns from rightTable to be added to the final result,expressed either as columnName or newColumnName=oldColumnName whenrenaming is desired or necessary.

In the join example shown in FIG. 13, leftTable employee table 1302 isjoined with rightTable department table 1312 with a join command 1320that creates the resultant table, table_C 1332. In this example, thevalues for last name 1304, department ID 1306, and telephone 1308 fromemployee table remain the same in table_C 1332. Each of the departmentID 1306 values in employee table 1302 have corresponding department ID1314 values in department table 1312 with the exception of the last rowof employee table 1302, “John” and “36”. Because a value for “36” doesnot exist in the department ID 1314 column of the department table 1312,a row for “John” and “36” in table_C does not exist because there was nomatch. Also, because the department table 1312 contains two rows for 31,sales, table_C contains two rows for Rafferty for 31 and sales with eachrow containing a different department telephone number.

It will be appreciated that the modules, processes, systems, andsections described above can be implemented in hardware, hardwareprogrammed by software, software instructions stored on a nontransitorycomputer readable medium or a combination of the above. A system asdescribed above, for example, can include a processor configured toexecute a sequence of programmed instructions stored on a nontransitorycomputer readable medium. For example, the processor can include, butnot be limited to, a personal computer or workstation or other suchcomputing system that includes a processor, microprocessor,microcontroller device, or is comprised of control logic includingintegrated circuits such as, for example, an Application SpecificIntegrated Circuit (ASIC), a field programmable gate array (FPGA),graphics processing unit (GPU), or the like. The instructions can becompiled from source code instructions provided in accordance with aprogramming language such as Java, C, C++, C#.net, assembly or the like.The instructions can also comprise code and data objects provided inaccordance with, for example, the Visual Basic™ language, a specializeddatabase query language, or another structured or object-orientedprogramming language. The sequence of programmed instructions, orprogrammable logic device configuration software, and data associatedtherewith can be stored in a nontransitory computer-readable medium suchas a computer memory or storage device which may be any suitable memoryapparatus, such as, but not limited to ROM, PROM, EEPROM, RAM, flashmemory, disk drive and the like.

Furthermore, the modules, processes systems, and sections can beimplemented as a single processor or as a distributed processor.Further, it should be appreciated that the steps mentioned above may beperformed on a single or distributed processor (single and/ormulti-core, or cloud computing system). Also, the processes, systemcomponents, modules, and sub-modules described in the various figures ofand for embodiments above may be distributed across multiple computersor systems or may be co-located in a single processor or system. Examplestructural embodiment alternatives suitable for implementing themodules, sections, systems, means, or processes described herein areprovided below.

The modules, processors or systems described above can be implemented asa programmed general purpose computer, an electronic device programmedwith microcode, a hard-wired analog logic circuit, software stored on acomputer-readable medium or signal, an optical computing device, anetworked system of electronic and/or optical devices, a special purposecomputing device, an integrated circuit device, a semiconductor chip,and/or a software module or object stored on a computer-readable mediumor signal, for example.

Embodiments of the method and system (or their sub-components ormodules), may be implemented on a general-purpose computer, aspecial-purpose computer, a programmed microprocessor or microcontrollerand peripheral integrated circuit element, an ASIC or other integratedcircuit, a digital signal processor, a hardwired electronic or logiccircuit such as a discrete element circuit, a programmed logic circuitsuch as a PLD, PLA, FPGA, PAL, or the like. In general, any processorcapable of implementing the functions or steps described herein can beused to implement embodiments of the method, system, or a computerprogram product (software program stored on a nontransitory computerreadable medium).

Furthermore, embodiments of the disclosed method, system, and computerprogram product (or software instructions stored on a nontransitorycomputer readable medium) may be readily implemented, fully orpartially, in software using, for example, object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer platforms. Alternatively,embodiments of the disclosed method, system, and computer programproduct can be implemented partially or fully in hardware using, forexample, standard logic circuits or a VLSI design. Other hardware orsoftware can be used to implement embodiments depending on the speedand/or efficiency requirements of the systems, the particular function,and/or particular software or hardware system, microprocessor, ormicrocomputer being utilized. Embodiments of the method, system, andcomputer program product can be implemented in hardware and/or softwareusing any known or later developed systems or structures, devices and/orsoftware by those of ordinary skill in the applicable art from thefunction description provided herein and with a general basic knowledgeof the software engineering and computer networking arts.

Moreover, embodiments of the disclosed method, system, and computerreadable media (or computer program product) can be implemented insoftware executed on a programmed general purpose computer, a specialpurpose computer, a microprocessor, or the like.

It is, therefore, apparent that there is provided, in accordance withthe various embodiments disclosed herein, methods, systems and computerreadable media for the dynamic updating of join operations.

Application Ser. No. 15/154,974, entitled “DATA PARTITIONING ANDORDERING” (Attorney Docket No. W1.1-10057) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,975, entitled “COMPUTER DATA SYSTEM DATASOURCE REFRESHING USING AN UPDATE PROPAGATION GRAPH” (Attorney DocketNo. W1.4-10058) and filed in the United States Patent and TrademarkOffice on May 14, 2016, is hereby incorporated by reference herein inits entirety as if fully set forth herein.

Application Ser. No. 15/154,979, entitled “COMPUTER DATA SYSTEMPOSITION-INDEX MAPPING” (Attorney Docket No. W1.5-10083) and filed inthe United States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,980, entitled “SYSTEM PERFORMANCE LOGGING OFCOMPLEX REMOTE QUERY PROCESSOR QUERY OPERATIONS” (Attorney Docket No.W1.6-10074) and filed in the United States Patent and Trademark Officeon May 14, 2016, is hereby incorporated by reference herein in itsentirety as if fully set forth herein.

Application Ser. No. 15/154,983, entitled “DISTRIBUTED AND OPTIMIZEDGARBAGE COLLECTION OF REMOTE AND EXPORTED TABLE HANDLE LINKS TO UPDATEPROPAGATION GRAPH NODES” (Attorney Docket No. W1.8-10085) and filed inthe United States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,984, entitled “COMPUTER DATA SYSTEM CURRENTROW POSITION QUERY LANGUAGE CONSTRUCT AND ARRAY PROCESSING QUERYLANGUAGE CONSTRUCTS” (Attorney Docket No. W2.1-10060) and filed in theUnited States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,985, entitled “PARSING AND COMPILING DATASYSTEM QUERIES” (Attorney Docket No. W2.2-10062) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,987, entitled “DYNAMIC FILTER PROCESSING”(Attorney Docket No. W2.4-10075) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,988, entitled “DYNAMIC JOIN PROCESSING USINGREAL-TIME MERGED NOTIFICATION LISTENER” (Attorney Docket No. W2.6-10076)and filed in the United States Patent and Trademark Office on May 14,2016, is hereby incorporated by reference herein in its entirety as iffully set forth herein.

Application Ser. No. 15/154,990, entitled “DYNAMIC TABLE INDEX MAPPING”(Attorney Docket No. W2.7-10077) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,991, entitled “QUERY TASK PROCESSING BASEDON MEMORY ALLOCATION AND PERFORMANCE CRITERIA” (Attorney Docket No.W2.8-10094) and filed in the United States Patent and Trademark Officeon May 14, 2016, is hereby incorporated by reference herein in itsentirety as if fully set forth herein.

Application Ser. No. 15/154,993, entitled “A MEMORY-EFFICIENT COMPUTERSYSTEM FOR DYNAMIC UPDATING OF JOIN PROCESSING” (Attorney Docket No.W2.9-10107) and filed in the United States Patent and Trademark Officeon May 14, 2016, is hereby incorporated by reference herein in itsentirety as if fully set forth herein.

Application Ser. No. 15/154,995, entitled “QUERY DISPATCH AND EXECUTIONARCHITECTURE” (Attorney Docket No. W3.1-10061) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,996, entitled “COMPUTER DATA DISTRIBUTIONARCHITECTURE” (Attorney Docket No. W3.2-10087) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,997, entitled “DYNAMIC UPDATING OF QUERYRESULT DISPLAYS” (Attorney Docket No. W3.3-10059) and filed in theUnited States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/154,998, entitled “DYNAMIC CODE LOADING”(Attorney Docket No. W3.4-10065) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/154,999, entitled “IMPORTATION, PRESENTATION,AND PERSISTENT STORAGE OF DATA” (Attorney Docket No. W3.5-10088) andfiled in the United States Patent and Trademark Office on May 14, 2016,is hereby incorporated by reference herein in its entirety as if fullyset forth herein.

Application Ser. No. 15/155,001, entitled “COMPUTER DATA DISTRIBUTIONARCHITECTURE” (Attorney Docket No. W3.7-10079) and filed in the UnitedStates Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/155,005, entitled “PERSISTENT QUERY DISPATCH ANDEXECUTION ARCHITECTURE” (Attorney Docket No. W4.2-10089) and filed inthe United States Patent and Trademark Office on May 14, 2016, is herebyincorporated by reference herein in its entirety as if fully set forthherein.

Application Ser. No. 15/155,006, entitled “SINGLE INPUT GRAPHICAL USERINTERFACE CONTROL ELEMENT AND METHOD” (Attorney Docket No. W4.3-10063)and filed in the United States Patent and Trademark Office on May 14,2016, is hereby incorporated by reference herein in its entirety as iffully set forth herein.

Application Ser. No. 15/155,007, entitled “GRAPHICAL USER INTERFACEDISPLAY EFFECTS FOR A COMPUTER DISPLAY SCREEN” (Attorney Docket No.W4.4-10090) and filed in the United States Patent and Trademark Officeon May 14, 2016, is hereby incorporated by reference herein in itsentirety as if fully set forth herein.

Application Ser. No. 15/155,009, entitled “COMPUTER ASSISTED COMPLETIONOF HYPERLINK COMMAND SEGMENTS” (Attorney Docket No. W4.5-10091) andfiled in the United States Patent and Trademark Office on May 14, 2016,is hereby incorporated by reference herein in its entirety as if fullyset forth herein.

Application Ser. No. 15/155,010, entitled “HISTORICAL DATA REPLAYUTILIZING A COMPUTER SYSTEM” (Attorney Docket No. W5.1-10080) and filedin the United States Patent and Trademark Office on May 14, 2016, ishereby incorporated by reference herein in its entirety as if fully setforth herein.

Application Ser. No. 15/155,011, entitled “DATA STORE ACCESS PERMISSIONSYSTEM WITH INTERLEAVED APPLICATION OF DEFERRED ACCESS CONTROL FILTERS”(Attorney Docket No. W6.1-10081) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein.

Application Ser. No. 15/155,012, entitled “REMOTE DATA OBJECTPUBLISHING/SUBSCRIBING SYSTEM HAVING A MULTICAST KEY-VALUE PROTOCOL”(Attorney Docket No. W7.2-10064) and filed in the United States Patentand Trademark Office on May 14, 2016, is hereby incorporated byreference herein in its entirety as if fully set forth herein.

While the disclosed subject matter has been described in conjunctionwith a number of embodiments, it is evident that many alternatives,modifications and variations would be, or are, apparent to those ofordinary skill in the applicable arts. Accordingly, Applicants intend toembrace all such alternatives, modifications, equivalents and variationsthat are within the spirit and scope of the disclosed subject matter.

1-22. (canceled)
 23. A memory and processor efficient computer system for dynamic updating of join operations, the system comprising: one or more processors; computer readable storage coupled to the one or more processors, the computer readable storage having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: receiving a join-based query directed to a remote query processor that contains two or more input tables to be joined; adding a node for each table providing input to the join operation to an update propagation graph; adding a join operation results node to the update propagation graph for holding results of executing the join-based query; adding a real-time merged notification listener for the join operation node in the update propagation graph; applying the join operation to the two or more input tables using indexes from the two or more input tables to identify and retrieve data needed for the join operation in order to minimize local memory and processor usage; using the real-time merged notification listener for the join operation node to listen for any changes to the joined two or more input tables in order to minimize local memory and processor usage by only conducting a join operation when a change has been detected; and when the real-time merged notification listener receives notification of changes to any of the joined two or more input tables, using indexes from the two or more input tables to apply the join operation only to the changes to update the join operation results node only for changed index ranges in order to minimize local memory and processor usage.
 24. The computer system of claim 23, wherein the join-based query is a left_join resulting in a table that has one column for each of a plurality of columns in a first input table's columns, and one or more new corresponding second input table columns with names that do not overlap or are renamed in order to not overlap with a name of one or more columns from a first input table; the one or more new corresponding second input table columns containing an aggregation of all values from the second input table that match a join criteria; and types of all newly created second input table columns not involved in the join criteria being an array of the second input table's column type.
 25. The computer system of claim 23, wherein the join-based query is an as_of_join resulting in a table that has one column for each of a plurality of columns in a first input table's columns, and one or more new corresponding second input table columns with names that do not overlap or are renamed in order to not overlap with a name of one or more columns from a first input table; the one or more new columns containing all values from the second input table that match a join criteria, the join criteria performing an exact match on all match columns except for one last match column of the match columns followed by performing a closest-less-than match on the last match column.
 26. The computer system of claim 23, wherein the join-based query is a reverse_as_of_join resulting in a table that has one column for each of a plurality of columns in a first input table's columns, and one or more new corresponding second input table columns with names that do not overlap or are renamed in order to not overlap with a name of one or more columns from a first input table; and the one or more new columns containing all values from the input table that match a join criteria, the join criteria performing an exact match on all match columns except for one last match column of the match columns followed by performing a closest-greater-than match on the last match column.
 27. The computer system of claim 23, wherein the join-based query is a range_as_of_join resulting in a table that has one column for each of a plurality of columns in a first input table's columns, and one or more new corresponding second input table columns with names that do not overlap or are renamed in order to not overlap with a name of one or more columns from a first input table; and the one or more new columns containing all values from the input table that match a join criteria, the join criteria returning each cell in the one or more new columns with an array of all values within a designated range for all match columns except for M match columns of the match columns where the match is exact, and the M match columns define a range match.
 28. The computer system of claim 23, wherein the join-based query is a natural_join resulting in a table that has one column for each of a plurality of columns in a first input table's columns, and one or more new corresponding second input table columns with names that do not overlap or are renamed in order to not overlap with a name of one or more columns from a first input table; the table having a same number of rows as the source table, the same number of rows containing an original content of the source table rows; and the one or more new columns determined by matching one or more values from the input table with the source table.
 29. The computer system of claim 23, wherein the join-based query is an exact_join resulting in a table that has one column for each of a plurality of columns in a first input table's columns, and one or more new corresponding second input table columns with names that do not overlap or are renamed in order to not overlap with a name of one or more columns from a first input table; the table having a same number of rows as the source table, the same number of rows containing an original content of the source table rows; the one or more new columns determined by matching one or more values from the input table with the source table; and the table containing exactly one match for each row with the input table.
 30. The computer system of claim 23, wherein the join-based query creates a subset filtered by a match criteria on a full Cartesian product, resulting in a table that has one column for each of a plurality of columns in a first input table's columns, and one or more new corresponding second input table columns with names that do not overlap or are renamed in order to not overlap with a name of one or more columns from a first input table.
 31. The computer system of claim 23, wherein the join operation node is different than the join operation results node.
 32. The computer system of claim 23, wherein the real-time merged notification listener for the join operation node is separate from the join operation node.
 33. The computer system of claim 23, wherein the real-time merged notification listener for the join operation node is separate from the join operation results node.
 34. The computer system of claim 23 wherein the operations of the remote query processor further include returning join operation results with strict ordering to guarantee ordering.
 35. The computer system of claim 23 wherein the operations of the remote query processor further include returning the join operation results that can contain arrays mapped to data.
 36. The computer system of claim 34 wherein the strict ordering is according to time.
 37. The computer system of claim 34 wherein the strict ordering is dictated by an order of data in the two or more input tables.
 38. The computer system of claim 23, wherein the changes include one or more of an add, modify, delete, or re-index.
 39. The computer system of claim 23, wherein the operations of the remote query processor further comprise automatically re-applying the join operation when the real-time merged notification listener detects any one of an add, modify, delete, or re-index message.
 40. The computer system of claim 23, further comprising when the two or more input tables are derived from a same ancestor table, changes in the same ancestor table cause a cascade of change notifications through the update propagation graph causing the remote query processor to combine the change notifications for efficiency and consistency.
 41. The computer system of claim 39, wherein the automatically re-applying is only applied to changed portions of the two or more input tables and not to unchanged portions.
 42. The computer system of claim 24, wherein the join criteria includes a formula.
 43. A method for dynamic updating of join operations, the method comprising: receiving a join-based query directed to a remote query processor that contains two or more input tables to be joined; adding a node for each table providing input to the join operation to an update propagation graph; adding a join operation results node to the update propagation graph for holding results of executing the join-based query; adding a real-time merged notification listener for the join operation node in the update propagation graph; applying the join operation to the two or more input tables using indexes from the two or more input tables to identify and retrieve data needed for the join operation in order to minimize local memory and processor usage; and using the real-time merged notification listener for the join operation node to listen for any changes to the joined two or more input tables in order to minimize local memory and processor usage by only conducting a join operation when a change has been detected.
 44. The method of claim 43, further comprising: sending a digital request for a remote query processor from a client computer to a remote query processor on a query server computer; automatically connecting the client computer to the remote query processor via a digital communications network, wherein the receiving includes receiving the join-based query digitally from the client computer to the remote query processor that contains two or more input tables to be joined.
 45. The method of claim 43, further comprising: the real-time merged notification listener receiving notification of changes to any of the joined two or more input tables; and after the real-time merged notification listener receives notification of changes to any of the joined two or more input tables, using indexes from the two or more input tables to apply the join operation only to the changes to update the join operation results node only for changed index ranges in order to minimize local memory and processor usage.
 46. A nontransitory computer readable medium having stored thereon software instructions that, when executed by one or more processors, cause the one or more processors to perform operations including: receiving a join-based query directed to a remote query processor that contains two or more input tables to be joined; adding a node for each table providing input to the join operation to an update propagation graph; adding a join operation results node to the update propagation graph for holding results of executing the join-based query; adding a real-time merged notification listener for the join operation node in the update propagation graph; applying the join operation to the two or more input tables using indexes from the two or more input tables to identify and retrieve data needed for the join operation in order to minimize local memory and processor usage; and using the real-time merged notification listener for the join operation node to listen for any changes to the joined two or more input tables in order to minimize local memory and processor usage by only conducting a join operation when a change has been detected.
 47. The nontransitory computer readable medium of claim 46, the operations further including: automatically connecting a client computer to the remote query processor via a digital communications network, wherein the receiving includes receiving the join-based query digitally from the client computer to the remote query processor that contains two or more input tables to be joined.
 48. The nontransitory computer readable medium of claim 46, the operations further including: after the real-time merged notification listener receives notification of changes to any of the joined two or more input tables, using indexes from the two or more input tables to apply the join operation only to the changes to update the join operation results node only for changed index ranges in order to minimize local memory and processor usage.
 49. A memory and processor efficient computer system for dynamic updating of join operations, the system comprising: one or more processors; computer readable storage coupled to the one or more processors, the computer readable storage having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: receiving a join-based query digitally directed to a remote query processor that contains two or more input tables to be joined; adding a node for each table providing input to the join operation to an update propagation structure; adding a join operation results node to the update propagation structure for holding results of executing the join-based query; adding a real-time merged notification listener for the join operation node in the update propagation structure; and when the real-time merged notification listener receives notification of changes to any of the joined two or more input tables, using indexes from the two or more input tables to apply the join operation only to the changes to update the join operation results node only for changed index ranges in order to minimize local memory and processor usage. 